ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
This document presents the research conducted to discriminate between reliable and unreliable models with the help of certain metrics. This was done by first eyeballing the prediction performance and then evaluating a number of mathematical metrics. Efforts were made to recognize the cause behind an unreliable model. Also research was conducted to improve the accuracy of the performance of the existing models.
A new application, described in this document, has been developed to automate the process of building thermal models for multiple transformers. These thermal models can then be used for transformer dynamic loading.
The second flow that was created was used to put together a flash block that is based off of a XILIX XCFXXP. This flow was somewhat similar to how the pad ring flow worked except that optimizations and a clock tree was added into the flow. There was a couple of design redoes due to timing and orientation constraints.
Finally, the last flow that was created was the top level flow which is where all of the components are combined together to create a finished test chip ready for fabrication. The main components that were used were the finished flash block, HERMES, test structures, and a clock instance along with the pad ring flow for the creation of the pad ring and power ring.
Also discussed is some work that was done on a previous multi-project test chip. The work that was done was the creation of power gaters that were used like switches to turn the power on and off for some flash modules. To control the power gaters the functionality change of some pad drivers was done so that they output a higher voltage than what is seen in the core of the chip.
clinical history and motor signs of tremor, rigidity and bradykinesia, and the estimated
number of patients living with Parkinson's disease around the world is seven
to ten million. Deep brain stimulation (DBS) provides substantial relief of the motor
signs of Parkinson's disease patients. It is an advanced surgical technique that is used
when drug therapy is no longer sufficient for Parkinson's disease patients. DBS alleviates the motor symptoms of Parkinson's disease by targeting the subthalamic nucleus using high-frequency electrical stimulation.
This work proposes a behavior recognition model for patients with Parkinson's
disease. In particular, an adaptive learning method is proposed to classify behavioral
tasks of Parkinson's disease patients using local field potential and electrocorticography
signals that are collected during DBS implantation surgeries. Unique patterns
exhibited between these signals in a matched feature space would lead to distinction
between motor and language behavioral tasks. Unique features are first extracted
from deep brain signals in the time-frequency space using the matching pursuit decomposition
algorithm. The Dirichlet process Gaussian mixture model uses the extracted
features to cluster the different behavioral signal patterns, without training or
any prior information. The performance of the method is then compared with other
machine learning methods and the advantages of each method is discussed under
different conditions.
In this thesis, the main objective is to develop the control model of the Energy Management System (EMS). The energy and power management in the FREEDM system is digitally controlled therefore all signals containing system states are discrete. The EMS is modeled as a discrete closed loop transfer function in the z-domain. A breakdown of power and energy control devices such as EMS components may result in energy con-sumption error. This leads to one of the main focuses of the thesis which is to identify and study component failures of the designed control system. Moreover, H-infinity ro-bust control method is applied to ensure effectiveness of the control architecture. A focus of the study is cyber security attack, specifically bad data detection in price. Test cases are used to illustrate the performance of the EMS control design, the effect of failure modes and the application of robust control technique.
The EMS was represented by a linear z-domain model. The transfer function be-tween the pricing signal and the demand response was designed and used as a test bed. EMS potential failure modes were identified and studied. Three bad data detection meth-odologies were implemented and a voting policy was used to declare bad data. The run-ning mean and standard deviation analysis method proves to be the best method to detect bad data. An H-infinity robust control technique was applied for the first time to design discrete EMS controller for the FREEDM system.